


"""simple docstring"""






from typing import Dict, Optional

import numpy as np

import datasets


_lowerCAmelCase							:    Dict					   =	'''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''

_lowerCAmelCase							:    Optional[Any]					   =	'''
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric("mean_iou")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
'''

_lowerCAmelCase							:    Optional[int]					   =	'''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = None			,		_lowerCamelCase = False			,		)    ->     List[str]:
   '''simple docstring'''





   if label_map is not None:
      for old_id, new_id in label_map.items():
         _lowerCamelCase   :				Union[str, Any]							=     new_id

    # turn into Numpy arrays
   _lowerCamelCase   :				Dict							=     np.array(_lowerCamelCase       )
   _lowerCamelCase   :				str							=     np.array(_lowerCamelCase       )

   if reduce_labels:
      _lowerCamelCase   :				Union[str, Any]							=     255
      _lowerCamelCase   :				Optional[Any]							=     label - 1
      _lowerCamelCase   :				Optional[Any]							=     255

   _lowerCamelCase   :				List[Any]							=     label != ignore_index
   _lowerCamelCase   :				int							=     np.not_equal(_lowerCamelCase			,		_lowerCamelCase       )
   _lowerCamelCase   :				Union[str, Any]							=     pred_label[mask]
   _lowerCamelCase   :				List[str]							=     np.array(_lowerCamelCase       )[mask]

   _lowerCamelCase   :				Union[str, Any]							=     pred_label[pred_label == label]

   _lowerCamelCase   :				int							=     np.histogram(_lowerCamelCase			,		bins=_lowerCamelCase			,		range=(0, num_labels - 1)       )[0]
   _lowerCamelCase   :				List[Any]							=     np.histogram(_lowerCamelCase			,		bins=_lowerCamelCase			,		range=(0, num_labels - 1)       )[0]
   _lowerCamelCase   :				Optional[Any]							=     np.histogram(_lowerCamelCase			,		bins=_lowerCamelCase			,		range=(0, num_labels - 1)       )[0]

   _lowerCamelCase   :				List[str]							=     area_pred_label + area_label - area_intersect

   return area_intersect, area_union, area_pred_label, area_label







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = None			,		_lowerCamelCase = False			,		)    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				List[Any]							=     np.zeros((num_labels,)			,		dtype=np.floataa       )
   _lowerCamelCase   :				List[Any]							=     np.zeros((num_labels,)			,		dtype=np.floataa       )
   _lowerCamelCase   :				int							=     np.zeros((num_labels,)			,		dtype=np.floataa       )
   _lowerCamelCase   :				Union[str, Any]							=     np.zeros((num_labels,)			,		dtype=np.floataa       )
   for result, gt_seg_map in zip(_lowerCamelCase			,		_lowerCamelCase       ):
      _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				Dict							=     intersect_and_union(
          _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )
      total_area_intersect += area_intersect
      total_area_union += area_union
      total_area_pred_label += area_pred_label
      total_area_label += area_label
   return total_area_intersect, total_area_union, total_area_pred_label, total_area_label







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = None			,		_lowerCamelCase = None			,		_lowerCamelCase = False			,		)    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				List[Any]							=     total_intersect_and_union(
       _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )

   # compute metrics
   _lowerCamelCase   :				List[Any]							=     {}

   _lowerCamelCase   :				Tuple							=     total_area_intersect.sum() / total_area_label.sum()
   _lowerCamelCase   :				List[Any]							=     total_area_intersect / total_area_union
   _lowerCamelCase   :				List[str]							=     total_area_intersect / total_area_label

   _lowerCamelCase   :				List[Any]							=     np.nanmean(_lowerCamelCase       )
   _lowerCamelCase   :				Dict							=     np.nanmean(_lowerCamelCase       )
   _lowerCamelCase   :				Union[str, Any]							=     all_acc
   _lowerCamelCase   :				List[Any]							=     iou
   _lowerCamelCase   :				Union[str, Any]							=     acc

   if nan_to_num is not None:
      _lowerCamelCase   :				str							=     {metric: np.nan_to_num(_lowerCamelCase			,		nan=_lowerCamelCase       ) for metric, metric_value in metrics.items()}

   return metrics




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,	_KWARGS_DESCRIPTION )
class 							A_      (       datasets.Metric ):


  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     return datasets.MetricInfo(
         description=_DESCRIPTION							,citation=_CITATION							,inputs_description=_KWARGS_DESCRIPTION							,features=datasets.Features(
             # 1st Seq - height dim, 2nd - width dim
             {
                 "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"    )    )    ),
                 "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"    )    )    ),
             }    )							,reference_urls=[
             "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
         ]							,)






  def 							_lowercase					(			self:						Any							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						Optional[int]							,__lowerCAmelCase:						int							,__lowerCAmelCase:						bool							,__lowerCAmelCase:						Optional[int] = None							,__lowerCAmelCase:						Optional[Dict[int, int]] = None							,__lowerCAmelCase:						bool = False							,):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[Any]							=     mean_iou(
         results=__lowerCAmelCase							,gt_seg_maps=__lowerCAmelCase							,num_labels=__lowerCAmelCase							,ignore_index=__lowerCAmelCase							,nan_to_num=__lowerCAmelCase							,label_map=__lowerCAmelCase							,reduce_labels=__lowerCAmelCase							,)
     return iou_result




'''simple docstring'''



def        UpperCAmelCase (    A	:					int					):
							if not isinstance(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   List[str]         =      F"""Input value of [number={number}] must be an integer"""
														raise TypeError(A					)
							if number < 0:
														return False
							SCREAMING_SNAKE_CASE       :   List[str]         =      number * number
							while number > 0:
														if number % 10 != number_square % 10:
																					return False
														number //= 10
														number_square //= 10
							return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()




